An Intelligent Prediction of Surface Roughness on Precision Grinding

Abstract:

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Precision grinding is generally used as the final finishing process, and it determines the surface quality of the machined component. It’s very difficult to achieve on-line measurement of the surface roughness. The purpose of this research was to study the surface roughness prediction and avoid the defect happening in the grinding process. A surface roughness prediction model was proposed in this paper, which presented the relationship between surface roughness and the wear condition of grinding wheel and grinding parameters. An AE sensor was used to collect the grinding signals during the grinding process to obtain the grinding wheel wear condition. Besides, a fuzzy neural network was used to obtain the prediction surface roughness. Grinding trials were performed on a high precision CNC cylindrical grinder (MGK1420) to evaluate the surface roughness prediction model. Experiment verified that the developed prediction system was feasible and had high prediction accuracy.

Info:

Periodical:

Solid State Phenomena (Volume 261)

Edited by:

Angelos P. Markopoulos and George Christopher Vosniakos

Pages:

221-225

DOI:

10.4028/www.scientific.net/SSP.261.221

Citation:

N. Ding et al., "An Intelligent Prediction of Surface Roughness on Precision Grinding", Solid State Phenomena, Vol. 261, pp. 221-225, 2017

Online since:

August 2017

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$35.00

* - Corresponding Author

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